Stator winding fault detection of induction generator based wind turbine using ANN - N. F. Fadzail; S. Mat Zali; M. A. Khairudin; N. H. Hanafi (Institute of Advanced Engineering and Science, 2020)
This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.